Systems and methods for coordinating signal injections to understand and maintain orthogonality among signal injections patterns in utility grids

ABSTRACT

Methods and systems for implementing experimental trials on utility grids. The variation of grid parameters are coordinated to create periods of time and areas of space from within which the variations of grid parameters do not overlap, allowing sensor data within those periods of time and areas of space to be associated with particular variations in grid parameters. This associated data can in turn be used to improve models of sensor response and utility grid behavior.

BACKGROUND

The performance of utilities grids—their reliability, safety, andefficiency—can be drastically improved through sensing key parametersand using those results to direct the operations and maintenance of thegrid, by identifying faults, directing appropriate responses, andenabling active management such as incorporating renewable sources intoelectrical grids while maintaining power quality.

Sensor networks are often used to monitor utilities grids. These sensornetworks may include smart meters located at the ends of the grid,sensors at grid nodes, and sensors on or around the utilities lines,these sensors measuring grid parameters such as flow rates in watergrids, power quality in electrical grids, or pressures in utilitiesgrids. These sensors are transducers, usually outputting analog signalsrepresentative of the measured properties. These outputs need to becharacterized to map to specific values of those properties, and/orclassified so that they may represent particular states of the world,such as a potential leak that requires investigation, or identificationof increases in reactive power when incorporating a renewable resourceinto an electrical grid. Characterization of sensors is usually donethrough bench testing, while the sensors may have various interferencesin the environment surrounding them; in-situ characterization of sensorson a utility grid monitoring network would be preferred, but isdifficult for the large numbers of sensors used to monitor a utilitiesgrid and the difficulty in accessing many of those sensors.

The trend in analyzing grid sensor data and directing responses is “bigdata,” which uses large amounts of grid historical data to build modelsused for classification and direction of responses. These big datamodels, however, are limited to correlations, as they mine historicaldata to build the models, limiting their effectiveness for activelydirecting treatments or making fine adjustments. Further, these big datamodels typically require large volumes of data that prevent highlygranular understandings of grid conditions at particular grid nodes orlocations or that can only achieve such granularity after longoperations; some have applied machine learning techniques and improvedmodels to increase speed and granularity, but even these approachescontinue to rely on correlations from passively collected historicaldata.

Signal injections have been used to highlight grid faults, such asdiscovering nodes where power is being illegally drawn from an AC powergrid; these techniques rely on already-characterized high-qualitysensors such as “smart meters” and are occasional, grid-wide individualactions, not coordinated to be conducted concurrently or sequentiallyand thus not suitable for in-situ calibration of a large number ofdiverse sensors. Signal injections have also been used to test grid-wideresponse to large changes in high levels of the grid, such as at theHVDC distribution level. Those signal injections have been large,individual, and human mediated, not susceptible to automation,smaller-scale local testing or concurrent or sequential implementationof tests, again inappropriate for calibrating and characterizing theresponses of individual local sensors in-situ. To adopt signal injectionfor regular in-situ characterization of sensors on a highly sensorizedgrid, there is a need to be able to inject signals concurrently andsequentially to increase sample sizes and enable automation withoutconfounding sensor responses with other signal injections.

Utilities grid management would benefit greatly from real-timecause-and-effect understanding of sensor responses to overcome theissues with big data smart grid approaches and allow for real-time,granular, and fine-tuned grid monitoring and management to more fullycapitalize on the potential of smart grid to optimize grid parametersand respond to potential grid pathologies, by enabling such optimizationto be done at more local levels across these highly variant systems.

SUMMARY

The present invention is directed towards the automated coordination ofsignal injections into a utilities grid to enable multiple concurrentand sequential tests of sensor response to grid events, by receiving aset of potential signal injections, computing spatial and temporalreaches for the signal injections, generating a set of signal injectionshaving non-overlapping reaches, and implementing the generated set ofsignal injections into the utility grid at the given times andlocations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of the steps of a method of the invention.

FIG. 2 is a map of a utility grid, its associated network of sensors,and the spatial reaches of signal injections selected for implementationby an example of the invention.

FIG. 3 is a system diagram of a system of the invention.

FIG. 4 is a data flow diagram of the flows of information among variouscomponents of a system of the invention.

FIG. 5 is a flowchart for an iterative process for assigning signalinjections to particular times and locations.

FIG. 6 is a diagram depicting the architecture of system embodiments andtheir interactions with a utility grid.

DETAILED DESCRIPTION

Signal injections provide a means of characterizing sensor responses orimproving sensor output classifications to improve understanding ofcurrent grid events on a utility grid monitored through a sensornetwork, such as “smart grid” efforts. This understanding may beimproved and refined most efficiently when tests of sensor response tosignal injections may be run concurrently and successively to maximizethe sample size and produce spatial and temporal granularity in theunderstanding of sensor outputs, but without these various testsconfounding one another's results. As a result, it is advantageous toautomatically coordinate multiple signal injections into a utility gridin time and space ensuring a larger sample size than would be otherwisepossible, while also ensuring that the samples themselves are notconfounded by overlapping signal injections. The resulting large numberof unconfounded experimental samples allows the generation ofactionable, cause-and-effect knowledge that has sufficient temporal andspatial granularity to drive localized optimization, realizing thepotential of “smart grid” optimization techniques for managing gridmaintenance, fault response, improvements of efficiency and providingricher intelligence to grid operators.

FIG. 1 is a flowchart of a method of the invention. A set of potentialsignal injections are received in step 100, spatial and temporal reachesare computed for the potential signal injections in step 102, aplurality of the potential signal injections are selected andcoordinated such that their spatial and temporal reaches do not overlapin step 104, and the signal injections are implemented into the utilitygrid in step 106. Optionally, sensor data may be collected within atleast the spatial and temporal reaches of the signal injections in step108. The sensor data is the output of sensors along the utility grid,such as the electrical waveforms output by transducers measuring gridparameters, or processed outputs from those sensors along the utilitygrid. Data collection may also be conducted continuously or over periodsor areas beyond the spatial and temporal reaches. Optionally, the sensordata may be associated with particular signal injections in step 110.

A set of potential signal injections received in step 100. The potentialsignal injections are data representative of the time, location andnature of signal injections that may be implemented across the networkto test models of sensor response. The nature of a signal injection isparticular to the type of utility grid the signal injection is madeinto, along with particular characteristics of the signal injection,such as its magnitude, or the utility grid variables changed by theinjection. The signal injections are controlled changes in gridparameters, for example, electrical signal injections in electricalgrids such as increases or decreases in current, voltage, or powerfactor caused by actuating grid controls.

Signal injections to be made into utility grids may be implementedautomatically through machine to machine controls (M2M), or may behuman-mediated in their implementation, occurring though automatedinstruction of grid personnel to carry out particular activities such asdeactivating a particular industrial load on an electrical grid. Thesesignal injections are controlled variations in grid conditions based onchanging aspects of grid operations, such as adjusting valves,activating power sources, or other such changes. These signal injectionsmay be performed on utility grids including gas grids, water grids, andelectrical grids. In gas grids, the signals may be injected through, forexample, changing the routing of gas through pipes to increase ordecrease the pressure at certain points. The responses to these signalsmay be the increase or decrease in the number and/or severity of leaksdetected by a sensor network surrounding the grid pipes, or changes indownstream pressures connected to the areas being driven to high or lowpressure. These signal injections may be accomplished in human-mediatedcases through the manual adjustment of various valves and switches atthe direction of a schedule distributed to maintenance personnel whoperform these adjustment; these schedules may take various forms, suchas maintenance queues, additional tasks, and may be distributed througha variety of electronic means such as email, text message, calendarreminders on a computer, tablet, smart phone or other portable computingdevice. In these human-mediated cases, the times of these adjustmentsmay be audited by having the maintenance personnel check in using anetworked device to record the time the changes are actuallyimplemented, for use in the processing of subsequent data generated as aresult of the grid response to these signal injections. In fullymachine-to-machine implemented embodiments of signal injection on gasgrids, the switches and valves are operated by actuators coupled to thesystem through a wired or wireless communications network, andresponding to signals sent by the system or acting in accordance withinstructions or schedules distributed to the controllers for thoseactuators by the system. Machine-to-machine implementations allow formore closely coordinated tests as there will be less variance in thetime of implementation, and the improved timing allows moresophisticated trials to be conducted. In these implementations,monitoring of the sensor conditions and actuator states may be constant,to create a real-time understanding of relationships among spatially andtemporally distributed influences, enabling changes in relationships aswell as local sensor states to be detected and characterized, forexample through factorial isolation of detected changes.

In electrical grids, human-mediated methods may include manual switchingof power flow, switching of capacitor banks or load tap changers,activating or deactivating power sources connected to the grid,activating or deactivating heavy industrial equipment having significanteffects on power draw (such as arc furnaces) or other majormanually-controlled power loads on the grid. In these examples, thechanges are made by the maintenance personnel at the direction of aschedule that is automatically generated and distributed to theappropriate maintenance personnel (e.g. those with access to andresponsibility for particular controls); these schedules may takevarious forms, such as maintenance queues, additional tasks, and may bedistributed through a variety of electronic means such as email, textmessage, calendar reminders on a computer, tablet, smart phone or otherportable computing device. In these human-mediated cases, the times ofthese adjustments may be audited by having the maintenance personnelcheck in using a networked device to record the time the changes areactually implemented, for use in the processing of subsequent datagenerated as a result of these signal injections. These human-mediatedmethods may alter measurable factors such as power quality, linetemperature, line sag, reactive power levels, and other factors, whichmay be captured by sensor networks observing those measurable gridfactors.

In electrical grids, machine-to-machine (M2M) methods offer a greatermeasure of control, and can automatically inject selected andcoordinated signals through a variety of means. This includes automationof the types of switching and maintenance behaviors that may be used inhuman-mediated examples such as automated switching of capacitor banksor selecting positions for load tap changers, and additionally M2Mmethods of signal injection may capitalize on greater precision andbreadth of control to include actions such as coordinating use ofdevices such as appliances at end locations to create coordinated demandand loading at consumer locations, or to implement complex coordinationof combinations of multiple types of grid-influencing actions togenerate more complex conditions, or introducing changes into theautomatic power factor correction units. These combinatoricpossibilities are very difficult to address through big-data approaches,since even large volumes of data may only have limited sample sizesreflecting particular combinations, and the sheer number of combinatoricpossibilities makes big data solutions to these problems nearlyintractable. These signal injections may be initiated through automaticcontrol of the associated grid components and networked devices,including power generation, switches, voltage regulation equipment suchas load tap changers and capacitor banks used for reactive powermanagement, smart meters and smart appliances receiving power from thegrid, and other grid components susceptible to remote control by thesystem. These may take advantage of millisecond-level controlcapabilities to manipulate power quality variables such as waveformshape, reactive power levels, RMS voltage and current levels, throughthe effects of changing positions on load tap changers, opening orclosing switches for particular capacitor banks, or the integration ofdistributed generation sources, addition or removal of new loads or thespecific operation of automatic power factor correction units

The injected signals may be simple, directing one individual grid actionsuch as opening a valve in a water or gas grid, or bringing oneparticular renewable source online by connecting it to the grid throughan actuated switch, or altering the output voltage from one substationin electrical grid examples to change the grid conditions, or they maybe complex, composed of multiple grid actions coordinated such thattheir individual spatial and temporal reaches overlap to produce amulti-factor treatment at areas within those overlapping reaches. Thismulti-factor treatment may include variances of multiple different gridparameters, for example adjusting the output from a substation whileconnecting a photovoltaic inverter to the grid just downstream, toexplore combinatoric effects of those parameters such as the effects ofthose example actions on the voltage waveform and level of reactivepower in that branch of the grid. Another example of a complex gridaction may be to vary both load tap changer positions and capacitor bankswitching simultaneously to provide more fine-grained control overreactive power in an electrical grid. Multi-factor treatments may beused to produce multiple instances of similar variations of a particulargrid parameter, for example to use additive effects to increase themagnitude of a particular variance of a grid parameter at one or morespecific locations on the grid while protecting more sensitiveneighboring parts of the grid by keeping them within narrower ordifferent operational ranges by exposing those parts to only a componentof the overall signal injection that is being made into the grid; forexample, the power levels at sensitive nodes around a more robust nodemay each be given an increase that has a predicted spatial reach thatincludes the more robust node, but not other sensitive nodes, and thesemultiple sensitive nodes may each provide a power increase within theirnarrower operational ranges to produce a combined increase in power atthe robust node that exceeds the individual increases at each sensitivenode.

For complex signals, the temporal and spatial reaches are predictedbased on treating the complex signal's effects on the system as a whole,composed set. For those complex signals, while individual grid actionswill have overlapping spatial and temporal reaches, the defined set ofgrid actions that make up the complex signal is instead treated as onesignal injection, with the overall spatial and temporal reach of thecombination of the defined set of grid actions used to determine theareas of space and periods of time where no other signals may beinjected into the grid, to maintain the orthogonality of the complexsignal injection from other grid signal injections.

Complex signals may be input into the system having already been definedas the set of grid actions to be done together and the times andlocations of those grid actions, after being derived by other systems orselected by grid personnel, or may be derived by systems selectingmultiple grid actions from the set of grid actions as directed by, forexample, a Partially Observable Markov Decision Process (POMDP) modelexploring combinatorics or operating within constraints on operationalconditions that vary from location to location across the grid.

Signal injections exploring grid responses may be composed by searchingfor waveforms that have a spatial-temporal regularity with anycontrolled grid activity, which are co-occurring in immediate or regulardelayed fashion, through for example, Principal Component or Fourieranalysis. These statistical regularities in waveforms or componentwaveforms (for example, the frequency, voltage, and/or current) linkgrid actions with changes in grid conditions to provide the set ofavailable options for manipulating grid conditions based on activecontrol of grid actions. Data on the observed times and locations ofthese waveform components relative to the grid actions may be used todetermine spatial and temporal reaches for particular signal injections.

For the signal injections, temporal and spatial reaches are computed instep 102, based on the nature of the signal injections and the grid. Thetemporal reach is the period over which the sensor network will beobserving events related to the injected signal. The temporal reachincludes the duration of the signal itself, and the duration of theexpected sensor response to the signal, including ongoing propagation ofthe signal, echoes, or other sensor responses associated with thesignal. This temporal reach may be computed by using the expected time,at a high confidence interval, of the duration over which the sensorwill be responding to the signal injection, in one example using a modelof grid components and inputting the signal injection into the model, orusing a historical model of the durations over which prior signalinjections were detected along the grid, and using that as the durationfor relevant data and a period from which to exclude other trials havinga common spatial reach. The spatial reach is the reach over which gridsensors are likely to show response to that signal; this may bepredicted through models that predict grid response to the signalinjection, such as a grid component model that then uses thecharacteristics of the grid elements and the nature of the signalinjection to calculate the area over which the signal injection willmanifest, or a historical data model based on observed spatial reachesin prior signal injections similar to the one having its reach computed.The spatial reach can then be controlled by predicting, to a highconfidence interval (for example, the 95% confidence interval), thefurthest sensor that would show a response to the injected signal andpreventing any other trial from being conducted if it is likely toproduce a response in the region of spatial reach during the period ofspatial uncertainty for the current trial. For example, for a signalinjection made by switching a capacitor bank on a distribution network,the spatial reach may be the downstream portion of the distributionnetwork, and an example of the temporal reach may be the time it takesfor transients introduced by the switching of the capacitor bank tosettle. These reaches are specific to the signal being injected, and thesignal may be complex, having multiple types and locations of input thatare all accounted for in setting the temporal and spatial scope, forexample altering the responses of power quality management units atsubstations in different manners when bringing renewable energy sourceson-line at a particular time; in this example, the spatial reach may bebased on the extent of the grid served by those substations even beyondthe reach of the power contribution from the renewable energy source,and the temporal uncertainty may include periods after the terminationof that renewable source's use, due to the effects of the power qualitymanagement units on the power wave form even after removal of therenewable source. Reaches may also be computed using historical data onobserved responses to specific grid actions, such as waveform componentsdiscovered through Fourier or Principal component analysis that arespatially and temporally proximate to previous instances of particulargrid actions.

A processor is used to coordinate a plurality of signal injections onthe grid, using the reach information as a constraint on assigningsignal injections to particular times and locations in step 104; theareas and periods of the spatial and temporal reaches of signalinjections are not allowed to overlap together, as such overlap couldintroduce confounds into the trials measuring sensor response to theinjected signals, since there would be multiple signals that couldpotentially be detected in those overlapping places and times,interfering with one another or creating uncertainty about what signalwas being detected. Note that both the temporal and spatial reaches mustoverlap for signals to confound one another; signals may overlap in timeif they do not overlap in space, and my overlap in space if there is nooverlap in time. The coordination of the signal injections is preferablydone through graphical modeling techniques, such as Principal ComponentsAnalysis, Bayesian networks or Markov random fields or subspeciesthereof, configured to maximize the parsimony and completeness of theset of selected non-interfering signal injections to be implementedacross the grid over a period of time. Other grid control activities ornatural variances in grid parameters which occur randomly with respectto the selected signal injections may continue to occur on the gridwithin the reaches of the coordinated signal injections.

Coordination of the signal injections may be done to implementparticular experimental trials in these non-confounding periods of spaceand time, to improve the understanding of grid conditions and sensorresponse. A Bayesian Causal Network may be used to look for dependenciesin the data to identify potentially valuable trials that may discovergrid control and sensor response knowledge. Systematic multivariateexperimentation is done to analyze the directionality and variablesinvolved in the underlying causal paths for those waveform components,by going back to the normative operational constraints and usingconstrained randomization, and experimental designs (such as LatinSquare) to systematically explore which grid control elements andcombinations thereof are the underlying cause of the waveforms. Theseexperimental designs may be iterated to refine the analysis, for exampleeliminating ¾ of the controls on a basic first pass, through eliminationof those controls that are random with respect to the waveformcomponents of interest, and then using factorial combinations of theremaining controls in a second trial to properly identify the control orcombination of controls causally linked to those waveform components ofinterest.

In another example, a Partially Observable Markov Decision Process(POMDP) could be used to sequentially make signal insertion decisions tocontinuously reduce uncertainty about the conditional dependencestructure among grid components. The POMDP may further be structured tomaximize the expected reward whereby the reward function is acombination of uncertainty reduction and other operational objectives.Other operational objectives include, for example, on electrical grids,load balancing, power quality optimization, renewable integration, andfault prediction indices; on water grids, flow optimization, lossprevention, management of infrastructure robustness; on gas grids,reserve management, leak prevention and minimization, and/or managementof infrastructure robustness. This management of signal insertionensures that signal injections will be coordinated such that they do notoverlap in their temporal and spatial uncertainties. Overlapping on oneof the dimensions, space or time, is an acceptable part of thecoordination of the network to maximize learning per time period byconducting multiple trials simultaneously, and conducting trialsback-to-back, but the signal injections must be separated on at leastone of either the temporal or spatial level to ensure that data can beproperly associated with a given signal injection and provide clean dataregarding the grid response to that signal injection, unconfounded byother signal injections. In some embodiments, the coordination of thesignals may allow overlap of the spatial and temporal reaches if thesignal injections that overlap in both dimensions affect different gridparameters that do not interact with one another, which may bedetermined from, for example, metadata for each signal injectionidentifying the affected grid parameters for that signal injection and atable of interacting grid parameters which may be based on theoreticalor observational data on the behavior of the grid parameters.

In some embodiments, the signal injections may also be constrained bythe operational ranges of the grid, in addition to the spatial andtemporal reaches of other signal injections. In these embodiments,normative operational constraint data is received and the predicted oractual grid conditions at the time of the signal injection are combinedwith the predicted effects of the signal injection, and the results ofthat combination may be compared to the permissible states of the gridto determine whether or not a signal injection can be assigned to aparticular time and location. Alternatively, the signal injectionsavailable for input may be restricted to include only control states orcombinations of control states that are used within the grid's normaloperational envelope, excluding the possibility to select signalinjections that would place controls into states outside of their normalranges.

FIG. 5 details one non-limiting example embodiment of a method forcoordinating the signal injections. In this example of an iterativeapproach to coordinating signals, a signal injection is selected 500,current grid conditions are received 502, and it is determined whetheror not the selected signal injection will be within grid constraintsbased on the grid conditions 504. If the selected signal injection iswithin grid constraints for the grid conditions, contemporaneous signalinjection data is received 506 and used to determine whether theselected signal injection overlaps with any current signals 508, and ifit does not overlap, the signal scheduled for insertion 510 andcontemporaneous signal injection data is updated 512. The processcontinues to iterate as long as there remain signal injections that maybe placed 514. If the signal injection is determined inconsistent withgrid constraints in step 504 or is determined to overlap with existingsignal injections in spatial and temporal reach in step 508, the signalinjection is rejected as a possibility and a new signal injection isselected if one remains to be placed.

A signal injection is selected in step 500. The signal injection may beselected from a table of potential signal injections, ranking the signalinjections by potential value or the required number of samples testingthat signal injection for a particular experimental design. Theselection may be made by proceeding through these ranked injections inorder, starting with the highest-priority signal injection.

Grid conditions are received in step 502. Grid conditions are currentmeasurements where signals are being selected for immediate injection,or predictions based on current models and/or historical data for thetime the signal injections are being selected and coordinated for. Gridconditions include operational parameters that must be kept withincertain ranges for normal operations, such as levels of reactive powerin electrical grids, which are needed to be maintained above certainthresholds for distribution.

The compliance of the selected signal injection with grid constraints isdetermined in step 504, based on the grid conditions. Each signalinjection has effects associated with it, for example a signal injectioninvolving the switching of load tap changers at a substation willincrease or decrease the available reactive power near that substation.These effects are added to the grid conditions received in step 502 andcompared to operational constraints. The operational constraints definethe permissible states of various grid parameters, for example thethreshold level of reactive power required to avoid a crash in powertransmission, or other aspects of normal operating conditions for agrid, such as pressures or flow rates at certain points on water or gasdistribution networks. In this example, the sum of the selected signalinjection effect and the grid conditions is compared to the constraints.If the sum is within the constraints, the signal injection is passed onto the next stage, and if not, the signal injection is rejected and thesystem moves on to the next possible signal injection, or if no signalinjection possibilities remain to be placed, the process ends, inaccordance with step 514. Contemporaneous signal injection data isreceived in step 506. The contemporaneous signal injection data is theongoing and/or planned signal injections and their spatial and temporalreaches. The contemporaneous signal injection data may be limited toongoing and/or planned signal injections which have a temporal reachincluding the time of the selected signal injection or which arescheduled to occur during the temporal reach of the selected signalinjection.

The spatial and temporal reaches of the selected signal injection arecompared with the contemporaneous signal injection data in step 508. Ifthe selected signal injection does not overlap with the contemporaneoussignal injection data in both spatial and temporal reach, then thesignal is approved for insertion and moves on to step 510. If theselected signal injection would overlap in both space and time with anongoing and/or planned signal injection, the selected signal injectionis rejected, and a new signal injection is selected to restart theprocess or the process ends in accordance with step 514.

Signals approved for insertion are scheduled for insertion in step 510.In this step, signals for immediate insertion are implementedimmediately, by either messaging human implementers or activating theappropriate actuators and other controls needed to effectuate theselected signal injection, for example activating the actuators oncapacitor bank switches at a substation on an electrical grid. Forexamples where the signals are being scheduled ahead of time, step 510involves scheduling the actions to take place at their designated time,either by adding the signal injection activities to the maintenancequeues or other directions provided to human implementers, or schedulingthe automated activities in M2M embodiments of this example.

The contemporaneous signal injection data is updated in step 512. Thisis done in this example by adding the signal scheduled for insertion instep 510 and its respective spatial and temporal reaches to the list ofsignal injections which are received in step 506 of subsequentiterations and against which the spatial and temporal reaches ofselected signal injections are compared to in step 508.

The process continues to iterate as long as there remain signalinjections that may be placed, which is determined in step 514. This isdetermined in this example by checking the table of potential signalinjections for signal injections that have not yet been selected andtested. If such signal injections exist, the selection step 500 isreturned to; once no more signal injections remain to attempt to beplaced for a particular time, the process ends.

Returning to FIG. 1, the coordinated signal injections are thenimplemented in the utility grid in step 106. The signals are theninjected into the sensor network according to the coordinated set ofsignal injections and upholding their temporal and spatial uncertaintyconstraints, by taking the directed grid actions at the proper times andlocations. The signal injections may be implemented by human actors,such as grid maintenance personnel, by directing them to perform thegrid actions such as hitting switches in electrical grids, or openingand closing valves on water and gas distribution grids, throughdistributing appropriate instructions to those grid personnel throughmeans such as email systems, automated messaging, queuing systems, orother means of instructing the human actors on what actions to take toinfluence the grid and when and where to implement them. The signalinjections may also be partially or wholly implemented throughmachine-to-machine actions, such as having processors direct the actionsof actuators controlling switches and valves, or controllersautomatically directing the activation of renewable sources or otherwiseimplementing the directed grid actions, based on signals and/or datadistributed to those processors and actuators, switches, sources andother grid components detailing the grid actions to take and the timeand location for those grid actions to be taken.

Sensor outputs are collected for at least the areas of the sensornetwork within the temporal and spatial uncertainty of the injectedsignals in step 108. Where data is collected continuously or for periodsthat include times and spaces outside the reaches of signal injectionson the network, the data from those times and locations may be parsedout from the sensor network data being collected continuously. Forsignal injections that have been coordinated to prevent overlappingspatial and temporal reaches, the response and the injected signal maybe associated based on the temporal and spatial reaches, as the reachesare used to prevent overlapping signal injections from confounding eachother, by ensuring a definite time and location over which the effectsof just one particular signal injection may be observed and allowingmultiple signal injections to be made concurrently, and for signalinjections to be made consecutively into the grid to increase samplesizes and knowledge without confounding the signal injections. Thisassociated data may be used to refine models of sensor response used tocharacterize or classify sensor outputs based on the sensor outputs andthe signal injections that influenced those sensor outputs. Alternateuses for the data include updating and refining models of grid responseto particular grid actions, or improving active control protocols tomaintain certain operational parameters or pursue operational goalsusing knowledge of the impact of the signal injection on grid conditionsthrough machine learning.

FIG. 2 is a diagram of a utility grid where multiple concurrent signalinjections are coordinated according to their spatial and temporalreaches, depicting the locations and reaches of coordinated signalinjections to be made concurrently into the grid at a point in time, toperturb the grid for observation and measurement without the signalinjections confounding the measurements of one another's effects, inaccordance with examples of the invention. The map 200 illustrates anarea with many lines and other grid elements, and electrical substationswhere automated action can be taken, located at 202, 206, and 210, withan example of the automated actions at those substations being switchingon or off particular capacitors and adjusting the position of load tapchangers to alter the phase of current and voltage with respect to oneanother and control the level of reactive power at that substation.Spatial reach 204 is computed for the selected load tap changer andcapacitor switch positions selected for a signal injection that is madeat substation 202, determined by historical data and/or models of theeffects of that selection and when and where its effects may be observedwithin a 95% confidence interval. Spatial reaches 208 and 212 arelikewise computed for the selected actions at taken at substation 206and substation 210 respectively. While the actions at all threesubstations are made concurrently and thus overlap temporally, thereaches do not overlap spatially, and thus grid responses to the signalinjections within each of the spatial reaches 204, 208, and 212 may beassociated with the signal injections and 202, 206, and 210 respectivelyto determine the response of sensors within the reach of each of thosesignal injection to those particular signal injections without themultiple concurrent signal injections confounding one another. If, forexample, during the coordination stage, the signal injection atsubstation 202 with spatial reach 204 was already selected, and apotential signal injection at 206 had a larger, overlapping spatialreach 214, the signal injection having spatial reach 214 would berejected in the coordination process and would not be able to beselected concurrently with the signal injection at 202 having spatialreach 206 because it would overlap, and thus that signal injection wouldbe rejected and another selected that had a reach which did not overlapexisting signal injection selections in space as well as time.

This coordination allows multiple signal injections to automatically beimplemented on the grid without compromising the effectiveness of thosesignal injections in refining models of sensor understanding and gridresponse to the signal injections, enabling sensing and control systemsto automatically produce multiple samples simultaneously andaccelerating the process of developing sensor understanding and eventclassification criteria on utility grids.

FIG. 3 is a diagram of an embodiment of the invention as a coordinatedutility grid system. Memories may be known computer storage means suchas flash memory, hard disk drives using magnetic media, or other methodsfor data storage that can store the data and be accessed frequently andregularly. Processors may be configured to make the calculations throughsoftware instructions. Connections among the components may behard-wired, use of common processors for multiple steps, or networkedthrough wired or wireless means such as the various 802.11 protocols,ZigBee or Bluetooth standards, Ethernet, or other known means fortransmitting data among the separate sensors, processors, memories andmodules. The sensors, memories, processors, and modules may bedistributed across locations, including at the sensors themselves, orco-located in intermediate or central locations.

Signal injection memory 300 stores the characteristics of signalinjections that may be made into the utility grid. This memory isconfigured to store the characteristics of potential signal injections,including the time, location, magnitude and parameters being affected bythe signal injection. This memory may also store implementation data forthe signal injection, such as the set of instructions to be presented togrid personnel for human-mediated embodiments, or the actuators andcommands to be distributed to them in machine-to-machine embodiments ofthe invention.

Grid Model Memory 302 stores grid information used to compute thespatial and temporal reaches of signal injections. The grid informationmay, for example, be stored as databases of grid characteristics, modelsof grid response, models of components and their interconnections, or aset of controllable grid actions with associated observed changes ingrid conditions such as components of overall power waveforms associatedwith grid actions in electrical grids, discovered through Fourier orPrincipal Component Analysis. Examples of the components andinterconnections used to predict reach on utility grids include pipelengths, pipe widths, and junction locations in water grids, pipelengths, pipe widths, and junction locations in gas grids, or sources,substations, connecting lines, the sources and sinks of current, forelectrical grids. Models of grid responses may be based on the physicalproperties of the utility and the grid components, and/or models basedon the historical spatial and temporal response characteristics of theutility grid to past grid actions.

Reach processor 304 computes the spatial and temporal reaches for signalinjections using grid properties or response models from the grid modelmemory 302 and signal injection characteristics by using those gridcharacteristics or models and the characteristics of the signalinjection to predict the periods of time and regions of space where thegrid response to the signal injection may be detected by sensors. Forexample, the reach processor may use the signal injectioncharacteristics to identify the grid actions that will be used toimplement the signal injection, and reference the historical data onthose grid actions to determine the previous observations regarding thespatial and temporal reaches of those particular types grid actions,then use those previous observations to predict the spatial and temporalreach for the signal injection.

Coordination Processor 306 is a processor configured to apply graphicalmodeling techniques, such as Bayesian networks or Markov random fieldsor subspecies thereof, to the set of signal injections and theircomputed regions to determine a set of signal injections to implementwhile maintaining the orthogonality of those signal injections throughplacing them in space and time such that the spatial and temporalreaches of the signal injections do not overlap.

Signal implementation means 308 may be either tools for distributing andensuring compliance with instructions governing the signal injectionsand their coordination across the utility grid in human mediatedembodiments, or may be processors, controllers, and actuators used toautomatically implement the signal injections in machine-to-machineembodiments of the invention. Examples include, for machine-to-machineexamples, actuators controlling valves in water and gas grids or controlcircuits and actuators situated at electrical substations such ascontrols for the positioning of load tap changers or switches forcapacitor banks used to manage reactive power, or switches controllingconnections between distributed power sources such as solar or windgenerators and the remainder of the grid. For human-mediatedembodiments, examples include automatic generation and distribution ofemails or text messages, computing devices carried by maintenancepersonnel and the servers they sync to for receiving queuinginstructions and reporting completion of tasks and status of the gridand/or completion of assigned maintenance tasks.

Sensor network 310 may optionally be a part of systems embodying theinvention. The sensor network may be a plurality of communicativelylinked individual network sensors 312, 314 and 316 which are distributedacross the utility grid to measure the grid parameters such as flowrates, current, voltage, line temperature, line sag, and whose outputmay reflect the changes in grid conditions resulting from signalinjections. These network sensors may be, for example, methanedetectors, sensored cable terminations, water flow meters, electrical“smart meters”, or other such grid sensors. These sensors monitorchanges in grid conditions stemming from the implemented signalinjections, and that data may be parsed according to the spatial andtemporal reaches of the signal injections based on the time and locationat which the sensor captures the data.

FIG. 4 is a data flow diagram showing the transfer of information amongelements of an example of the invention as a coordinated utility gridsystem, and the transformation of that information at each element toautomatically coordinate and implement signal injections into a utilitygrid.

Signal injection characteristics 400 is data describing the signalinjections that may be implemented on the grid, including informationsuch as the location, including the magnitude, time, location, andnature of the signal injections. The nature of the signal injections mayinclude the particular actions taken to manipulate the grid parametersor the particular grid parameters to be manipulated to implement thesignal injection. The signal injection characteristics 400 are stored insignal injection memory 402, and are transferred to the reach processor404 and optionally to the coordination processor 406. At the reachprocessor, the signal injection characteristics and the gridcharacteristics 408 from the grid model memory 410 are used to computethe spatial and temporal reaches 412 for a given signal injection in aparticular location.

Spatial and Temporal Reaches 412 define the period of time and area ofspace that will be affected by a particular signal injection. These areinitially defined at the reach processor 404 which predicts the periodof time and area of space, and is then sent to the coordinationprocessor 406, which arranges spatial and temporal reaches intonon-overlapping coordinated sets of signal injections 414.

Coordinated Signal Injections 414 are definitions of the time and placeat which to implement particular signal injections into the grid, andthe details of implementing those signal injections. The details ofimplementing the signal injections may be instructions to be distributedto maintenance resources that will be taking the required actions forhuman implemented embodiments, or may be machine instructions forcontrolling the actuators and other elements that will be implementingthe signal injections in machine-to-machine embodiments of theinvention. The times and locations for the signal injections aredetermined by the coordination processor 406 while the instructions arebased on the signal injection characteristics 400, and are sent to thesignal implementation means 416, for either direct machine-to-machineimplementation of the selected signal injections at the directed timesand places, or for the scheduling or queuing of maintenance resourcesand distribution to those resources that will be implementing the signalinjections in embodiments where the signals are injected by humanactors.

A simple example of an overall architecture involving an exampleembodiment of the invention is presented in FIG. 6. The control decisionlayer 600 makes decisions about the states for some or all girdcontrols. Grid control decisions are made according to methods ensuringthat the manipulation of controls creates samples that do not influenceone another, and optionally selecting the control decisions to providehigh learning value or to improve particular grid parameters such asensuring certain voltage levels in electrical grids, or flow rates ingas or water grids. The control decisions from the control decisionlayer 600 are carried out by the controls 602, 604, and 606. Examples ofparticular controls include capacitor bank switches, load tap changers,switches and storage devices on electrical grids, or valves and sourceson water and gas grids. The controls may carry out the control decisionsby, for example, actuating switches, moving load tap changer positions,and narrowing or widening valves. The actions of the controls changegrid parameters, and those changes propagate through the grid 608. Forexample, opening a valve on a gas grid may cause pressures to increasedownstream over time, within a certain distance from the valve, or in anelectrical grid, power quality and reactive power levels may changebased on the switching on or off of a capacitor bank. Sensors 614, 616,and 618 placed along the grid measure grid parameters, and detect thepropagation of the signal injection through the grid 608. The signalinjections are limited in the extent to which they propagate through thegrid 608, defined as the spatial reach of that signal injection such asthe spatial reach 610 outlining the region affected by the signalinjected by control 602 and including the connection of sensor 614 tothe grid 608, and spatial reach 612 outlining the region affected by thesignal injected by control 606 and including the connection of sensor618 to grid 608. Data processing layer 620 associates the data fromsensors 614, 616, and 618 with signal injections whose spatial andtemporal reaches include the sensor data, for example associating datafrom sensor 614 with data from a signal injection implemented by control602 based on spatial reach 610, and associating data from sensor 618with a signal injection implemented by control 606 based on spatialreach 612. The associated sensor data from the data processing layer 620is then analyzed by the data analysis layer 622 to determineunderstandings about grid behavior and sensor response. Thisunderstanding of grid behavior generated by the data analysis layer 622may, for example, take the form of sensor response models which are usedto interpret the outputs from grid sensors 614, 616, and 618 duringordinary operations, for example to set thresholds or alerts forbrownout conditions when voltage drops in an electrical line, or settingan alert for methane levels crossing normal operational thresholds. Thedata analysis layer 622 may interface with the control decision layer600 to iteratively coordinate and implement signal injections into thegrid and provide information that improves the selection of signalinjections to implement, for example by predicting the effects of asignal injection on the grid or computing the extent to which learningmay be refined by a particular signal injection.

1. A method for delivering coordinated signal injections into a utilitygrid, comprising: receiving spatial and temporal reaches for a pluralityof signal injections; selecting times and locations for the plurality ofsignal injections to be implemented into a utility grid such that thespatial and temporal reaches of the signal injections do not overlap;and implementing the signal injections into the utility grid at theselected times and locations.
 2. The method of claim 1, furthercomprising collecting data from sensors along the utility grid.
 3. Themethod of claim 2, further comprising associating the collected datafrom sensors with signal injections, based on the time and location ofthe sensor data and the spatial and temporal reaches of the signalinjection.
 4. The method of claim 1, wherein the signal injection isimplemented by changing the state of grid controls.
 5. The method ofclaim 4 wherein grid controls are capacitor banks
 6. The method of claim4, wherein grid controls are load tap changers
 7. The method of claim 4,wherein grid controls are inverters
 8. The method of claim 1, whereinimplementing the signal injections is dispatching grid personnel toperform a task.
 9. The method of claim 1, wherein a graphical modelingtechnique is used for selecting the times and locations the plurality ofsignal injections are to be implemented.
 10. The method of claim 9,wherein the graphical modeling technique is a Bayesian Causal Network11. The method of claim 1, wherein a Partially Observable MarkovDecision Process is used for selecting the times and locations theplurality of signal injections are to be implemented.
 12. The method ofclaim 1, wherein the spatial reach is computed based on a database ofutility grid response to prior signal injections.
 13. The method ofclaim 1, wherein the temporal reach is computed based on a database ofutility grid response to prior signal injections.
 14. The method ofclaim 1, wherein at least some of the plurality of the signal injectionsare made into the utility grid simultaneously
 15. A system for makingcoordinated signal injections into a utility grid, comprising: a memoryconfigured to store spatial reaches for a plurality of signalinjections; a memory configured to store temporal reaches for aplurality of signal injections; a processor configured to select a setof times and locations for signal injections within which the temporalreaches and spatial reaches are not both overlapping; and a plurality ofcontrols on a utility grid which implement the signal injections at theselected times and locations.
 16. The system of claim 15, furthercomprising a plurality of sensors along the utility grid.
 17. The systemof claim 16, further comprising a processor configured to associate datafrom the plurality of sensors with signal injections.
 18. The system ofclaim 15, wherein the processor is configured to select the times andlocations for signal injection using a graphical modeling technique. 19.The system of claim 15, further comprising a processor configured tocompute a spatial reach for a signal injection based on a database ofgrid response to prior signal injections.
 20. The system of claim 15,further comprising a processor configured to compute a temporal reachfor a signal injection based on a database of grid response to priorsignal injections.